Genetic Algorithm with Species for Regularization Network Metalearning

0356026 - ÚI 2011 RIV DE eng C - Conference Paper (international conference)
Neruda, Roman - Vidnerová, Petra
Genetic Algorithm with Species for Regularization Network Metalearning.
Advances in Information Technology. Berlin: Springer, 2010 - (Papasratorn, B.; Lavangnananda, K.; Chutimaskul, W.; Vanijja, V.), s. 192-201. Communications in Computer and Information Science, 114. ISBN 978-3-642-16698-3. ISSN 1865-0929.
[IAIT 2010. International Conference on Advances in Information Technology /4./. Bangkok (TH), 04.11.2010-05.11.2010]
R&D Projects: GA ČR GA201/08/1744
Institutional research plan: CEZ:AV0Z10300504
Keywords : regularization * neural networks * metalearning * genetic algorithms
Subject RIV: IN - Informatics, Computer Science

Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, though their drawback is the presence of metaparameters. The metaparameters are typically supposed to be given by an user. In this paper, we develop a method for finding optimal values for metaparameters, namely type of kernel function, kernel’s parameter and regularization parameter. The method is based on genetic algorithms with different species for different kinds of kernels. The method is demonstrated on experiments.
Permanent Link: http://hdl.handle.net/11104/0194655